The flag you mentioned seems to do the trick. Thanks for your answer !

Lucas

2017-05-23 17:47 GMT-04:00 Frédéric Bastien <[email protected]>:

> Your process isn't kicked out. With the old GPU back-end, by default when
> we free GPU memory, it return to the driver, so another process can take it.
>
> You can use the Theano flag lib.cnmem=N, where if N is greater then 1, it
> will the memory it will preallocate and don't return to the driver. If it
> is between 0 and 1, then it is a % of the total GPU memory that will be
> reserved.
>
> On Thu, May 18, 2017 at 8:32 PM Lucas Caccia <[email protected]> wrote:
>
>> Hi,
>>
>> I'm currently using Theano and Lasagne, and I'm always facing the same
>> problem : whenever someone else launches a job on the same GPU, my process
>> gets kicked out : I  get  "error allocating X bytes of memory". At first, I
>> thought it was because I was loading 1 minibatch at a time, and another
>> process was grabbing the memory before I could fit the next minibatch on
>> the GPU. So, I'm now storing my dataset in a shared variable, and
>> referencing it with the "givens" keywork in my theano function. However,
>> I'm still facing the same allocation problem. Is there any way to
>> preallocate memory such that theano won't have to do so at every single
>> training batch iteration ?
>>
>>
>> Thanks,
>> Lucas
>>
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